<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[AtotheI]]></title><description><![CDATA[AtotheI]]></description><link>https://www.atothei.com/blog</link><generator>RSS for Node</generator><lastBuildDate>Tue, 04 Aug 2026 05:31:38 GMT</lastBuildDate><atom:link href="https://www.atothei.com/blog-feed.xml" rel="self" type="application/rss+xml"/><item><title><![CDATA[Joint-Embedding Predictive Architectures Offer Path Beyond Current LLM Limitations]]></title><description><![CDATA[In an interview published in Dædalus, Yann LeCun and James Manyika detail why current generative models fall short and argue that Joint-Embedding Predictive Architectures provide a superior path forward. LeCun highlights that early convolutional neural networks reduced frontal car collisions by 40 percent, demonstrating the transformative potential of vision architectures over pure language prediction models. Why Are Current Large Language Models Inherently Limited? Mainstream artificial...]]></description><link>https://www.atothei.com/post/joint-embedding-predictive-architectures-offer-path-beyond-current-llm-limitations</link><guid isPermaLink="false">6a67a4a5b59210289e95faf9</guid><pubDate>Mon, 27 Jul 2026 18:34:37 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/6bf3e1_749b104be43a488fa30f18e0a8a1b591~mv2.jpg/v1/fit/w_960,h_640,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Miguel Nogueira</dc:creator></item></channel></rss>